The conjugate gradient regularization method in computed tomography problems

DOI10.1016/S0096-3003(98)10007-3zbMath0932.65138OpenAlexW2164676763MaRDI QIDQ1294241

Elena Loli Piccolomini, Fabiana Zama

Publication date: 21 March 2000

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0096-3003(98)10007-3

zbMATH Keywords

comparison of methodscomputed tomographynumerical examplesinverse problemsTikhonov regularizationregularization methodsstopping criterionconjugate gradient iterations

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Radon transform (44A12) Numerical methods for integral transforms (65R10) Numerical methods for inverse problems for integral equations (65R32)

Related Items (9)

Representation of high resolution images from low sampled Fourier data: applications to dynamic MRI ⋮ An experiment of parallel SPECT data reconstruction ⋮ On unifying randomized methods for inverse problems ⋮ Investigation of temperature parallel simulated annealing for optimizing continuous functions with application to hyperspectral tomography ⋮ Application of simulated annealing for multispectral tomography ⋮ A B-spline parametric model for high resolution dynamic magnetic resonance imaging ⋮ A weighted randomized Kaczmarz method for solving linear systems ⋮ Randomized Kaczmarz Converges Along Small Singular Vectors ⋮ A comparison of regularization models for few-view CT image reconstruction

Uses Software

• Regularization tools

Cites Work

• Unnamed Item
• An iterative algorithm for large size least-squares constrained regularization problems
• SIRT- and CG-type methods for the iterative solution of sparse linear least-squares problems
• The Mathematics of Computerized Tomography
• Truncated Singular Value Decomposition Solutions to Discrete Ill-Posed Problems with Ill-Determined Numerical Rank
• LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
• Picture Reconstruction from Projections
• On the Numerical Solution of Ill-Conditioned Linear Systems with Applications to Ill-Posed Problems

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